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<b>Incidence of type 2 diabetes with verapamil compared with other calcium channel blockers</b>

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Version 2 2025-11-05, 15:04
Version 1 2025-10-30, 18:12
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posted on 2025-11-05, 15:04 authored by Julian W. Sacre, John M. Wentworth, Dianna J. Magliano, Jonathan E. Shaw
<p dir="ltr">Objective. Evidence that the calcium channel blocker (CCB) verapamil slows type 1 diabetes progression suggests possible preventive benefits among people at risk of type 2 diabetes. We compared type 2 diabetes incidence between users of verapamil vs. other CCBs in a population-based cohort.</p><p dir="ltr">Research Design and Methods. From a random sample of Australians in national subsidized healthcare databases, we identified 90026 individuals who initiated treatment with a CCB (≥2 supplies) between July-2003 and December-2014. Incident diabetes was captured by subsequent receipt of glucose-lowering treatment or registration on the National Diabetes Services Scheme. Individuals were followed from first CCB supply until discontinuation, diabetes onset, death, or end-2014. Multi-state Poisson regression models characterised associations of CCB subclass with type 2 diabetes incidence and death (competing event), after multivariable propensity score adjustment.</p><p dir="ltr">Results. The cohort comprised 4485 verapamil users (5.0%) and 85541 treated with other CCBs (mostly dihydropyridines). During a median 1.6-year follow-up, 101 verapamil-treated individuals developed type 2 diabetes (8.8 per 1000 person-years) compared with 2622 people treated with other CCBs (11.4 per 1000 person-years). This translated to an incidence rate ratio of 0.77 (95% CI 0.63 to 0.94) in favor of verapamil (fully adjusted), and a lower probability of type 2 diabetes at 6 years (4.2% [95% CI 3.3%–5.3%] vs. 5.4% [4.7%–6.3%] for a typical clinical profile; absolute risk difference: 1.3% [95% CI –0.1%–2.4%]). Results were robust across multiple sensitivity analyses.</p><p dir="ltr">Conclusions. Verapamil use is associated with a lower incidence of type 2 diabetes compared with other CCBs. </p><p><br></p>

Funding

This work was partially supported by the Victorian Government’s Operational Infrastructure Support Program and National Health and Medical Research Council of Australia (NHMRC) Grant 1002663. During these analyses, JES and DJM were supported by NHMRC Investigator Grants (APP1173952 to JES, and APP2016668 to DJM). JMW was supported by JDRF Australia (4-SRA-2022-1246-M-N) and the Medical Research Future Fund (RARUR000103).

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